1. Field of the Invention
The present invention relates to the recognition of fingerprints, which is a well know biometric technique for the identification of individuals. The present invention more specifically relates to an automatic identity authentication method and system by recognition of fingerprints based on a comparison of a current fingerprint with one or several previously stored reference fingerprints.
2. Discussion of the Related Art
In the past, fingerprint recognition would essentially be used as a proof and identification element in criminal cases. Now, fingerprints are also used as a means of identification and authentication of an individual in access control or in secure authentication systems.
The fingerprint recognition is performed by examining the papillary line arrangement which forms characteristic points called minutiae. At least 8 (generally, between 8 and 17) of such minutiae of the papillary arrangement with no mismatching between two prints are necessary for the prints to be considered as being identical.
13 different types of minutiae have been counted. The 6 most frequent minutiae are the end, the independent groove, the branch, the spur, the lake, and the crossing. From a statistical point of view, minutiae of branch and end type are a majority in a fingerprint. Therefore, in automated fingerprint recognition processes, the matching of the minutiae of branch and end type is essentially examined.
FIG. 1 very schematically shows an automatic fingerprint recognition system of the type to which the present invention applies. Such a system is essentially formed of a sensor 1 (formed, for example, of an optical device of digitizer type) connected to a processing unit 2 (PU) in charge of interpreting the measurement results. Unit 2 provides, over a connection 3, a signal of authentication or non-authentication of a finger D laid on sensor 1. Processing unit 2 generally has the function of shaping the graphical image generated by sensor 1 and of analyzing this image to compare it with one or several images contained in a reference database.
FIG. 2 illustrates, in the form of blocks, a conventional example of a fingerprint recognition method of the type to which the present invention applies. Sensor 1 provides a graphical image 10 in which the grooves s (peaks and valleys) of the papillary arrangement are shown in shades of grey. Processing unit 2 then performs a digital preprocessing of the image (most often a filtering and a binarization followed by a thinning down or a squeletization of the grooves) to obtain a view (block 11) exploitable for the minutia extraction. In a next step (block 12), the position of the minutiae in the image is determined. In FIG. 2, the minutiae are symbolized by triangles b when they are branches and by crosses t when they are ends. The actual recognition is performed based on a mapping 13 of the minutiae, which are compared (block 14, MATCH) with one or several reference mappings 15 contained in a database (DB). Result R of the comparison is provided to the application having required the fingerprint authentication.
The present invention more specifically relates to the comparison (block 14, MATCH) of a current minutia mapping with a reference minutia mapping. The obtaining of this mapping and the minutia extraction is performed by any known method. Among these known methods, the method described in article “Minutia Matching Algorithm in Fingerprint Verification” by X. Luo, J. Tian, and Y. Wu, published in 2000 in Proceedings of the International Conference on Pattern Recognition and incorporated herein by reference may be mentioned as an example.
A first known method to compare a current minutia mapping with a reference mapping consists of selecting a point of the current mapping, then transforming the mapping into polar coordinates so that the selected point becomes the center of the image. A point of the reference set is then selected and this set is also transformed into polar coordinates. All modules and angles of the different points (minutiae in both spaces) are then compared to determine possible matchings.
A first disadvantage of this method is that all the minutiae of the reference set must be selected by being successively taken as an origin to compare the mapping with that of the current set. This requires a great number of comparisons.
A second disadvantage is that the point selected as the origin in the current image may, instead of being a minutia, be an artifact of the image. The previously-mentioned great number of comparisons must thus be performed by successively taking all the points of the current set as the origin of the polar coordinates to be sure that there is no matching between the two fingerprints.
The above first method enables interrupting the algorithmic processing as soon as a matching of a sufficient number of minutiae has been found. However, it must be carried on with all minutiae to be sure that there is no matching. The required time is then relatively long (on the order of one second).
A second known method consists of applying a so-called generalized Hough transform to examine the power required to pass from one minutia mapping to the other. Such a method is described, for example, in article “A real-time matching system for large fingerprint databases” by A. K. Jain, S. Shaoyun, K. Karu, and N. K. Ratha, published in IEEE Transactions on pattern analysis and machine intelligence, August 1996 (Vol. 18, N° 8) which is incorporated herein by reference. The implementation of such a method requires a lot of calculations and takes even more time (several seconds) than the former.